Dan Guo

1.1k citations
8 papers · 781 indexed · 1 hit paper · h-index 5

Impact in

Papers in

Dan Guo

7 papers receiving 766 citations

Hit Papers

Data-Driven Evolutionary Optimization: An Overview and Case Studies 2018 · 457 citations
4572018202620202023100200300400

Peers

Dan Guo
Comparison fields: 5 of 83
  • Computational Theory and Mathematics 540
  • Artificial Intelligence 527
  • Management Science and Operations Research 131
  • Statistics, Probability and Uncertainty 37
  • Industrial and Manufacturing Engineering 47
Replace Saúl Zapotecas–Martínez with:
Saúl Zapotecas–Martínez Mexico
Md Asafuddoula Australia
Ricardo Landa Becerra Mexico
Rituparna Datta India
Xilu Wang Germany
Gregorio Toscano‐Pulido Mexico
Lie Meng Pang China
Zefeng Chen China
Krishnendu Sanyal India
Dan Guo relative to Saúl Zapotecas–Martínez Mexico Saúl Zapotecas–Martínez's profile →
Citations per field
00.5×1.6×
Saúl Zapotecas–Martínez · 1×
Citations per year

Countries citing papers authored by Dan Guo

Since Specialization
Citations

This map shows the geographic impact of Dan Guo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dan Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Guo more than expected).

Fields of papers citing papers by Dan Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Guo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dan Guo. The network helps show where Dan Guo may publish in the future.

Co-authors

The 18 scholars most cited alongside Dan Guo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Guo Line = papers co-authored together Dan Guo links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
Data-Driven Evolutionary Optimization: An Overview and Case Studies
Hit paper breakdown →
2018457
2 2018178
3 2021121
4 202215
5 20157
6 20192
7 20241
8 20210

About Dan Guo

Dan Guo is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Signal Processing, having authored 8 papers that have together received 781 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (3 papers), Advanced Multi-Objective Optimization Algorithms (3 papers), Evolutionary Algorithms and Applications (2 papers), Advanced Adaptive Filtering Techniques (1 paper), Speech and Audio Processing (1 paper), Remote-Sensing Image Classification (1 paper), Face and Expression Recognition (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Computational Theory and Mathematics (540 citations), Artificial Intelligence (527 citations), Management Science and Operations Research (131 citations), Statistics, Probability and Uncertainty (37 citations) and Industrial and Manufacturing Engineering (47 citations). Dan Guo has collaborated with scholars based in China, United Kingdom and Singapore. Frequent co-authors include Yaochu Jin, Handing Wang, Kaisa Miettinen, Tinkle Chugh, Jinliang Ding, Tianyou Chai, Yun-Lin Wang, Pengfei Wang, Huazhong Zhang and Beilei Yuan. Their work appears in journals such as IEEE Transactions on Cybernetics, Speech Communication, Molecules, IEEE Transactions on Systems Man and Cybernetics Systems and IEEE Transactions on Evolutionary Computation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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